Here we publish the data used in paper "Junming Huang, Gavin Cook, and Yu Xie, Large-scale Quantitative Evidence of Media Impact on Public Opinion toward China". This dataset include estimated sentiments on The New York Times on China in eight topics from 1970 to 2019, and a time series of public attitude aggregated from surveys on China.
Hvasta, M. G.; Slighton, N. T.; Kolemen, E.; Fisher, A. E.
Rotating Lorentz-force flowmeters are a novel and useful technology with a range of
applications in a variety of different industries. However, calibrating these flowmeters can
be challenging, time-consuming, and expensive. In this paper, simple calibration procedures
for rotating Lorentz-force flowmeters are presented. These procedures eliminate the need for
expensive equipment, numerical modeling, redundant flowmeters, and system down-time.
The calibration processes are explained in a step-by-step manner and compared to experimental results.
This setup mimics ice lying above the drainage system. In the experiment, a fluid-filled blister is generated via liquid injection into the interface between a transparent elastic layer and a porous substrate. After injection of liquid, the fluid permeates from the blister through the porous substrate, the blister volume V(t) relaxes exponentially with time. Our lab experiments show that varying the permeability of the porous substrate k significantly impacts the relaxation timescale in the experiments.
In this paper, hydraulic jump control using electromagnetic force in a liquid metal flow is presented. The control methods used give insight into the hydraulic jump behavior in the presence of magnetic fields and electrical currents. Flowing liquid metals is a proposed solution to heat flux challenges posed in fusion reactors, specifically the tokamak. Unfortunately, thin, fast-flowing liquid metal divertor concepts for fusion reactors are susceptible to hydraulic jumps that drastically reduce the liquid metal flow speed, leading to potential problems such as excessive evaporation, unsteady power removal, and possible plasma disruption. Highly electrically conductive flows within the magnetic fields do not exhibit traditional hydraulic jump behavior. There is very little research investigating the use of externally injected electrical currents and magnetic fields to control liquid metal hydraulic jumps. By using externally injected electrical currents and a magnetic field, a Lorentz force (also referred to as j × B force) may be generated to control the liquid metal jump behavior. In this work, a free-surface liquid metal—GaInSn eutectic or “galinstan”—flow through an electrically insulating rectangular duct was investigated. It was shown that applying a Lorentz force has a repeatable and predictable impact on the hydraulic jump, which can be used for liquid metal control within next-generation fusion reactors.
Pereira, Talmo D.; Aldarondo, Diego E.; Willmore, Lindsay; Kislin, Mikhail; Wang, Samuel S.-H.; Murthy, Mala; Shaevitz, Joshua W.
Recent work quantifying postural dynamics has attempted to define the repertoire of behaviors performed by an animal. However, a major drawback to these techniques has been their reliance on dimensionality reduction of images which destroys information about which parts of the body are used in each behavior. To address this issue, we introduce a deep learning-based method for pose estimation, LEAP (LEAP Estimates Animal Pose). LEAP automatically predicts the positions of animal body parts using a deep convolutional neural network with as little as 10 frames of labeled data for training. This framework consists of a graphical interface for interactive labeling of body parts and software for training the network and fast prediction on new data (1 hr to train, 185 Hz predictions). We validate LEAP using videos of freely behaving fruit flies (Drosophila melanogaster) and track 32 distinct points on the body to fully describe the pose of the head, body, wings, and legs with an error rate of <3% of the animal's body length. We recapitulate a number of reported findings on insect gait dynamics and show LEAP's applicability as the first step in unsupervised behavioral classification. Finally, we extend the method to more challenging imaging situations (pairs of flies moving on a mesh-like background) and movies from freely moving mice (Mus musculus) where we track the full conformation of the head, body, and limbs.